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  4. Should service firms introduce algorithmic advice to their existing customers? The moderating effect of service relationships
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Should service firms introduce algorithmic advice to their existing customers? The moderating effect of service relationships

Type
journal article
Date Issued
2023
Author(s)
Von Walter, Benjamin  
;
Wentzel, Daniel  
;
Raff, Stefan
DOI
10.1016/j.jretai.2023.05.001
Abstract
An increasing number of service firms are introducing algorithmic advice to their customers. In this research, we examine the introduction of such tools from a relational perspective and show that the type of relationship a customer has with a service firm moderates his or her response to algorithmic advice. Studies 1 and 2 find that customers in communal relationships are more reluctant to use algorithmic advice instead of human advice than customers in exchange relationships. Study 3 shows that offering customers algorithmic advice may harm communal relationships but not exchange relationships. Building on these findings, Studies 4, 5, and 6 examine how firms can mitigate the potentially negative relational consequences of algorithmic advice. While a fallback option that signals that customers can request additional human advice if needed is effective in preventing relational damages in communal relationships, this same intervention backfires in exchange relationships. These findings have important implications by showing that managers need to consider the relational consequences of introducing algorithmic advice to existing customers.
Keywords
Algorithmic advice
Robo-advisor
Service relationships
Professional services
HSG Classification
contribution to scientific community
Volume
99
Number
2
Start page
280
End page
296
Official URL
https://www.sciencedirect.com/science/article/pii/S0022435923000179
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/118473
Subject(s)

business studies

File(s)
Thumbnail Image
Name

2023_vonWalter_et_al_Should service firms introduce algorithmic advice to their existing customers.pdf

Size

920.73 KB

Format

Adobe PDF

Checksum (MD5)

9c841214d979a2698920402b3421e066

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